User Collaboration in Information Commons

Despite early emphasis on decentralized organization and individual contributions, information commons such as online encyclopedias and open-source projects increasingly rely on users working in groups to collectively generate content as a shared resource. However, current insights into institutions as a driver of group performance are limited. First, the effect of institutions is often assessed in the aggregate rather than delineated by type. This ignores the fact that different rules govern different aspects of group behavior, leading to potential discrepancies in their relative effect on group outcomes. Second, groups vary not only in the individual rules they adopt but also in global attributes. Some groups exhibit greater institutional instability than others. Groups also differ in how diverse and how balanced the rules are.

Guided by the IAD approach, we develop and test several propositions linking rules to outcomes in an information commons. As the largest online encyclopedia in China, Baidu Baike operates as an open platform and relies on individual contributors to build and update encyclopedic entries. Based on a new dataset tracking changes in the rules and performance of 40 self-organized user groups over an eight-year period, our analysis shows that the effects on group performance are typologically imbalanced, in that significant variation in group performance is attributed to only some rule types. As for the global attributes, having a larger and more diverse set of rules generally corresponds with better group performance, while institutional instability does not seem to exert any discernible impact.